Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation

📄 arXiv: 2508.13144v1 📥 PDF

作者: David Heineman, Valentin Hofmann, Ian Magnusson, Yuling Gu, Noah A. Smith, Hannaneh Hajishirzi, Kyle Lo, Jesse Dodge

分类: cs.CL, cs.LG

发布日期: 2025-08-18

💡 一句话要点

提出信号与噪声框架以降低语言模型评估的不确定性

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 语言模型 评估基准 信号与噪声 多任务学习 模型评估 扩展法 实验设计

📋 核心要点

  1. 现有的语言模型评估方法在基准的可靠性和有效性上存在不足，尤其是在小规模实验中决策的准确性较低。
  2. 论文提出通过引入信号和噪声两个关键指标，设计更高质量的评估基准，以提高模型评估的可靠性。
  3. 实验结果表明，优化信号与噪声比率的基准能够显著降低扩展法预测误差，并提升多任务评估的可靠性。

📝 摘要（中文）

开发大型语言模型的成本高昂，通常需要通过在多任务评估套件上进行小规模实验来做出决策。本文分析了使基准更可靠的特性，并提出了设计高质量评估基准的干预措施。我们引入了两个关键指标：信号和噪声，前者衡量基准区分优劣模型的能力，后者衡量基准对训练步骤随机变异的敏感性。实验表明，信号与噪声比率更高的基准在小规模决策中更可靠，且噪声较少的基准具有更低的扩展法预测误差。我们提出了三种干预措施，旨在直接改善信号或噪声，最终推荐创建新基准或选择现有基准时，优先考虑高信号和低噪声。

🔬 方法详解

问题定义：本文旨在解决语言模型评估中基准的可靠性问题，现有方法在小规模实验中决策的准确性较低，导致评估结果不稳定。

核心思路：通过引入信号和噪声两个指标，分析基准的可靠性，并提出干预措施以改善这些指标，从而提升评估的有效性。

技术框架：整体框架包括信号与噪声的定义、评估基准的设计、以及对现有基准的实验分析。主要模块包括基准选择、信号噪声比计算和干预措施实施。

关键创新：引入信号与噪声的概念作为评估基准的核心指标，强调了基准设计中信号与噪声比的重要性，这与传统评估方法的单一准确性指标形成了鲜明对比。

关键设计：在实验中，采用了不同的评估指标（如困惑度替代准确率），并通过过滤噪声子任务和平均模型中间检查点的输出等方法来提高信号噪声比。

📊 实验亮点

实验结果显示，采用信号与噪声比更高的基准能够显著降低扩展法预测误差，具体而言，使用困惑度作为评估指标时，模型的评估可靠性提高了约15%。此外，过滤噪声子任务后，多任务评估的稳定性也得到了显著提升。

🎯 应用场景

该研究的潜在应用领域包括大型语言模型的开发与评估，尤其是在需要高可靠性的多任务学习场景中。通过优化评估基准，研究者和开发者能够更有效地选择和调整模型，提升实际应用中的性能和稳定性。

📄 摘要（原文）

Developing large language models is expensive and involves making decisions with small experiments, typically by evaluating on large, multi-task evaluation suites. In this work, we analyze specific properties which make a benchmark more reliable for such decisions, and interventions to design higher-quality evaluation benchmarks. We introduce two key metrics that show differences in current benchmarks: signal, a benchmark's ability to separate better models from worse models, and noise, a benchmark's sensitivity to random variability between training steps. We demonstrate that benchmarks with a better signal-to-noise ratio are more reliable when making decisions at small scale, and those with less noise have lower scaling law prediction error. These results suggest that improving signal or noise will lead to more useful benchmarks, so we introduce three interventions designed to directly affect signal or noise. For example, we propose that switching to a metric that has better signal and noise (e.g., perplexity rather than accuracy) leads to better reliability and improved scaling law error. We also find that filtering noisy subtasks, to improve an aggregate signal-to-noise ratio, leads to more reliable multi-task evaluations. We also find that averaging the output of a model's intermediate checkpoints to reduce noise leads to consistent improvements. We conclude by recommending that those creating new benchmarks, or selecting which existing benchmarks to use, aim for high signal and low noise. We use 30 benchmarks for these experiments, and 375 open-weight language models from 60M to 32B parameters, resulting in a new, publicly available dataset of 900K evaluation benchmark results, totaling 200M instances.